class_docstr: Beam search decoder for RNN-T model with biasing support.
  
  Args:
      model (RNNT): RNN-T model to use.
      blank (int): index of blank token in vocabulary.
      temperature (float, optional): temperature to apply to joint network output.
          Larger values yield more uniform samples. (Default: 1.0)
      hypo_sort_key (Callable[[Hypothesis], float] or None, optional): callable that computes a score
          for a given hypothesis to rank hypotheses by. If ``None``, defaults to callable that returns
          hypothesis score normalized by token sequence length. (Default: None)
      step_max_tokens (int, optional): maximum number of tokens to emit per input time step. (Default: 100)
      trie (list, optional): the prefix tree for TCPGen biasing
      biasing (bool, optional): If true, do biasing, otherwise use standard RNN-T support
docstr: Performs beam search for the given input sequence in streaming mode.
  
  T: number of frames;
  D: feature dimension of each frame.
  
  Args:
      input (torch.Tensor): sequence of input frames, with shape (T, D) or (1, T, D).
      length (torch.Tensor): number of valid frames in input
          sequence, with shape () or (1,).
      beam_width (int): beam size to use during search.
      state (List[List[torch.Tensor]] or None, optional): list of lists of tensors
          representing transcription network internal state generated in preceding
          invocation. (Default: ``None``)
      hypothesis (Hypothesis or None): hypothesis from preceding invocation to seed
          search with. (Default: ``None``)
  
  Returns:
      (List[Hypothesis], List[List[torch.Tensor]]):
          List[Hypothesis]
              top-``beam_width`` hypotheses found by beam search.
          List[List[torch.Tensor]]
              list of lists of tensors representing transcription network
              internal state generated in current invocation.
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code: def infer(
      self,
      input: torch.Tensor,
      length: torch.Tensor,
      beam_width: int,
      state: Optional[List[List[torch.Tensor]]] = None,
      hypothesis: Optional[Hypothesis] = None,
  ) -> Tuple[List[Hypothesis], List[List[torch.Tensor]]]:
      r"""Performs beam search for the given input sequence in streaming mode.
  
      T: number of frames;
      D: feature dimension of each frame.
  
      Args:
          input (torch.Tensor): sequence of input frames, with shape (T, D) or (1, T, D).
          length (torch.Tensor): number of valid frames in input
              sequence, with shape () or (1,).
          beam_width (int): beam size to use during search.
          state (List[List[torch.Tensor]] or None, optional): list of lists of tensors
              representing transcription network internal state generated in preceding
              invocation. (Default: ``None``)
          hypothesis (Hypothesis or None): hypothesis from preceding invocation to seed
              search with. (Default: ``None``)
  
      Returns:
          (List[Hypothesis], List[List[torch.Tensor]]):
              List[Hypothesis]
                  top-``beam_width`` hypotheses found by beam search.
              List[List[torch.Tensor]]
                  list of lists of tensors representing transcription network
                  internal state generated in current invocation.
      """
      if input.dim() != 2 and not (
          input.dim() == 3 and input.shape[0] == 1
      ):
          raise ValueError("input must be of shape (T, D) or (1, T, D)")
      if input.dim() == 2:
          input = input.unsqueeze(0)
  
      if length.shape != () and length.shape != (1,):
          raise ValueError("length must be of shape () or (1,)")
      if length.dim() == 0:
          length = length.unsqueeze(0)
  
      enc_out, _, state = self.model.transcribe_streaming(
          input, length, state
      )
      return self._search(enc_out, hypothesis, beam_width), state
  
imports: from typing import Callable, Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import RNNT
  from torchaudio.prototype.models.rnnt import TrieNode
label_desc: Use image node_id 11 for calling the RNNTBeamSearchBiasing obj's underlying member method code with example usage: obj.infer(input, length, beam_width, state, hypothesis) and returns: self,  state